DRL has seen an incredible recent success on some robotics tasks such as manipulation [1]} and navigation [2]}, albeit predominantly in simulation. In order for a DRL agent to be trained in simulation and deployed on a real robot either 1) the reality gap is small or is overcome with training techniques such as DR such that no refinement of the policy is needed on the real robot (zero-shot transfer), or 2) the agent policy is fine-tuned on the real robot after primarily training in simulation. The latter option reduces the burden on the simulation fidelity and training regime, but fine-tuning on the robot can be difficult or impossible if the reward is determined by leveraging ground truth parameters (like localization) that are only available in the simulator.
In this work, we argue that the framework of DRL holds potential for the task of active localization, and can be used to train policies that transfer to a robot in real-world indoor environments. Borrowing concepts from Bayes filtering [3]}, we define a reward as a function of the posterior belief. Specifically, our reward is defined as the probability of posterior belief at ground truth pose.
